Abstract
Traditional Chinese Medicine (TCM) started considering the medicinal and health effects of food thousands of years ago. TCM labels are placed on foods based on cold, neutral, and hot properties similar to Chinese herbal medicine. However, it is unclear whether such a classification has any molecular or biochemical basis, and what the relationship is between this TCM classification and the nutrient composition of food. To answer these questions, we collected a large dataset, in which each type of food has both TCM labels and molecular composition records for statistical analyses and machine-learning predictions. We applied machine-learning methods by using food molecular composition to predict the hot, neutral or cold label of food, and achieved more than 80% accuracy, which clearly indicated that TCM labels have a significant molecular basis. We also applied ANOVA to analyze the main factors contributing to the TCM labels. The ANOVA analysis shows that some molecular/biochemical compositions and categories, such as Energy, Fat, Protein, Water and Selenium (Se), have the strongest correlations with the TCM labels of food. To the best of our knowledge, this study represents the first effort to quantitatively explore the relationship between TCM labels and the molecular composition of food.
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References
Hobert, O. (2008). Gene regulation by transcription factors and microRNAs. Science, 319(5871), 1785–1786.
Ergil, M. C., & Ergil, K. (2009). Pocket atlas of Chinese medicine. New York: Thieme.
Yang, S. (1965). Grand simplicity of inner canon of Huangdi. Beijing: People’s Medical Publishing House.
Zhu, J., Deng, W., et al. (2015). Theoretical origination of medicine and food homology. Journal of Traditional Chinese Medicine University of Hunan, 35(12), 27–30.
Yu, H., Zhou, H., Xiao, X., Lit, T., Yuan, H., Zhao, Y., & Gao, X. (2001). Advances and prospects of four properties of Chinese traditional medicine. China Journal of Basic Medicine in Traditional Chinese Medicine, 7(8), 61–64.
Li, S., Zhang, Z. Q., Wu, L. J., Zhang, X. G., Li, Y. D., & Wang, Y. Y. (2007). Understanding ZHENG in traditional Chinese medicine in the context of neuro-endocrine-immune network. IET Systems Biology, 1(1), 51–60.
Liang, Y. (1998). Study on the therapeutic mechanism of hyperthermia. Chinese Journal of Integrated Traditional and Western Medicine, 18(5), 305–306.
He, F., Deng, K., et al. (2008). The status and prospect on studies of the four properties for the traditional Chinese Materia medical. Chinese Journal of Experimental Traditional Medical Formulae, 14(8), 72–75.
Wang, H. (2006). Encyclopedia of healthcare based on Chinese food. Guangzhou: Guangdong Travel and Tourism Press.
Nutrition Data. (2016). SELF nutrition data know what you eat. http://nutritiondata.self.com/
Yang, Y. (2009). China food composition. Beijing: Peking University Medical Press.
USDA, Agriculture Research Service. (2019). Software developed by the National Agricultural Library v.3.9.5.1_2019-04-03. Retrieved from https://ndb.nal.usda.gov/ndb/
Webmagic (2016). A scalable web crawler framework for Java. Retrieved from http://webmagic.io/en/
Fisher, R. A. (1921). On the probable error of a coefficient of correlation deduced from a small sample. Metron, 1, 3–32.
Hinkelmann, K., & Kempthorne, O. (2008). Design and analysis of experiments. I and II (2nd ed.). New York: Wiley.
Moore, D. S., & McCabe, G. P. (2003). Introduction to the practice of statistics. New York: WH Freeman.
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In The workshop on computational learning theory (Vol. 5, pp. 144–152). New York: ACM.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.
Chang, C. C., & Lin, C. J. (2011). LIBSVM: A library for support vector machines (Vol. 2, pp. 1–27). New York: ACM.
Ho, T. K. (1995). Random decision forests. In Proceedings of the 3rd international conference on document analysis and recognition, Montreal, QC, 14–16 (pp. 278–282). Piscataway, NJ: IEEE.
Hastie, T., Tibshirani, R., & Friedman, J. (2008). The elements of statistical learning 2nd end. Berlin: Springer.
Lin Y, Jeon Y (2002). Random forests and adaptive nearest neighbors (Technical Report No. 1055). University of Wisconsin .
Chen T (2016). Machine learning challenge winning solutions. Retrieved from https://github.com/dmlc/xgboost/tree/master/demo#machine-learning-challenge-winning-solutions
Chen T (2016). XGBoost introduction. Retrieved August 1, 2016, from http://homes.cs. washington.edu/~tqchen/2016/03/10/story-and-lessons-behind-the-evolution-of-xgboost.html
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105). Cambridge, MA: MIT.
Acknowledgements
This work has been partially supported by the National Natural Science Foundation of China (61503150, 61972174), the Jilin Scientific and Technological Development Plan (20160520012JH, 20170204057GX), the Guangdong Key-Project for Applied Fundamental Research (Grant 2018KZDXM076), the Guangdong Premier Key-Discipline Enhancement Scheme (Grant 2016GDYSZDXK036) and the Paul K. and Dianne Shumaker Endowed Fund at University of Missouri.
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Han, X., Zhao, H., Xu, H., Yang, Y., Liang, Y., Xu, D. (2020). Molecular Basis of Food Classification in Traditional Chinese Medicine. In: Zhao, Y., Chen, DG. (eds) Statistical Modeling in Biomedical Research. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-33416-1_10
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